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32,402 | A General Framework for Density Based Time Series Clustering Exploiting
a Novel Admissible Pruning Strategy | cs.LG | Time Series Clustering is an important subroutine in many higher-level data
mining analyses, including data editing for classifiers, summarization, and
outlier detection. It is well known that for similarity search the superiority
of Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes as
we consider... | computer science |
32,403 | Learning Operations on a Stack with Neural Turing Machines | cs.LG | Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed
recently to address the difficulty of storing information over long time
periods. In this paper, we experiment with the capacity of Neural Turing
Machines (NTMs) to deal with these long-term dependencies on well-balanced
strings of parentheses. ... | computer science |
32,404 | Success Probability of Exploration: a Concrete Analysis of Learning
Efficiency | cs.LG | Exploration has been a crucial part of reinforcement learning, yet several
important questions concerning exploration efficiency are still not answered
satisfactorily by existing analytical frameworks. These questions include
exploration parameter setting, situation analysis, and hardness of MDPs, all of
which are unav... | computer science |
32,405 | Trained Ternary Quantization | cs.LG | Deep neural networks are widely used in machine learning applications.
However, the deployment of large neural networks models can be difficult to
deploy on mobile devices with limited power budgets. To solve this problem, we
propose Trained Ternary Quantization (TTQ), a method that can reduce the
precision of weights ... | computer science |
32,406 | Learning to superoptimize programs - Workshop Version | cs.LG | Superoptimization requires the estimation of the best program for a given
computational task. In order to deal with large programs, superoptimization
techniques perform a stochastic search. This involves proposing a modification
of the current program, which is accepted or rejected based on the improvement
achieved. Th... | computer science |
32,407 | Cryptocurrency Portfolio Management with Deep Reinforcement Learning | cs.LG | Portfolio management is the decision-making process of allocating an amount
of fund into different financial investment products. Cryptocurrencies are
electronic and decentralized alternatives to government-issued money, with
Bitcoin as the best-known example of a cryptocurrency. This paper presents a
model-less convol... | computer science |
32,408 | Diagnostic Prediction Using Discomfort Drawings | cs.LG | In this paper, we explore the possibility to apply machine learning to make
diagnostic predictions using discomfort drawings. A discomfort drawing is an
intuitive way for patients to express discomfort and pain related symptoms.
These drawings have proven to be an effective method to collect patient data
and make diagn... | computer science |
32,409 | An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical
Structures | cs.LG | We propose online algorithms for sequential learning in the contextual
multi-armed bandit setting. Our approach is to partition the context space and
then optimally combine all of the possible mappings between the partition
regions and the set of bandit arms in a data driven manner. We show that in our
approach, the be... | computer science |
32,410 | Implicit Modeling -- A Generalization of Discriminative and Generative
Approaches | cs.LG | We propose a new modeling approach that is a generalization of generative and
discriminative models. The core idea is to use an implicit parameterization of
a joint probability distribution by specifying only the conditional
distributions. The proposed scheme combines the advantages of both worlds -- it
can use powerfu... | computer science |
32,411 | Learning Adversary-Resistant Deep Neural Networks | cs.LG | Deep neural networks (DNNs) have proven to be quite effective in a vast array
of machine learning tasks, with recent examples in cyber security and
autonomous vehicles. Despite the superior performance of DNNs in these
applications, it has been recently shown that these models are susceptible to a
particular type of at... | computer science |
32,412 | Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance
Reduction | cs.LG | Zeroth-order (derivative-free) optimization attracts a lot of attention in
machine learning, because explicit gradient calculations may be computationally
expensive or infeasible. To handle large scale problems both in volume and
dimension, recently asynchronous doubly stochastic zeroth-order algorithms were
proposed. ... | computer science |
32,413 | Efficient Non-oblivious Randomized Reduction for Risk Minimization with
Improved Excess Risk Guarantee | cs.LG | In this paper, we address learning problems for high dimensional data.
Previously, oblivious random projection based approaches that project high
dimensional features onto a random subspace have been used in practice for
tackling high-dimensionality challenge in machine learning. Recently, various
non-oblivious randomi... | computer science |
32,414 | Control Matching via Discharge Code Sequences | cs.LG | In this paper, we consider the patient similarity matching problem over a
cancer cohort of more than 220,000 patients. Our approach first leverages on
Word2Vec framework to embed ICD codes into vector-valued representation. We
then propose a sequential algorithm for case-control matching on this
representation space of... | computer science |
32,415 | Combinatorial semi-bandit with known covariance | cs.LG | The combinatorial stochastic semi-bandit problem is an extension of the
classical multi-armed bandit problem in which an algorithm pulls more than one
arm at each stage and the rewards of all pulled arms are revealed. One
difference with the single arm variant is that the dependency structure of the
arms is crucial. Pr... | computer science |
32,416 | Towards Information-Seeking Agents | cs.LG | We develop a general problem setting for training and testing the ability of
agents to gather information efficiently. Specifically, we present a collection
of tasks in which success requires searching through a partially-observed
environment, for fragments of information which can be pieced together to
accomplish vari... | computer science |
32,417 | Design of Data-Driven Mathematical Laws for Optimal Statistical
Classification Systems | cs.LG | This article will devise data-driven, mathematical laws that generate
optimal, statistical classification systems which achieve Bayes' error rate for
data distributions with unchanging statistics. Thereby, I will design learning
machines that minimize the Bayes' risk or probability of misclassification. I
will devise a... | computer science |
32,418 | An empirical analysis of the optimization of deep network loss surfaces | cs.LG | The success of deep neural networks hinges on our ability to accurately and
efficiently optimize high-dimensional, non-convex functions. In this paper, we
empirically investigate the loss functions of state-of-the-art networks, and
how commonly-used stochastic gradient descent variants optimize these loss
functions. To... | computer science |
32,419 | DizzyRNN: Reparameterizing Recurrent Neural Networks for Norm-Preserving
Backpropagation | cs.LG | The vanishing and exploding gradient problems are well-studied obstacles that
make it difficult for recurrent neural networks to learn long-term time
dependencies. We propose a reparameterization of standard recurrent neural
networks to update linear transformations in a provably norm-preserving way
through Givens rota... | computer science |
32,420 | An Architecture for Deep, Hierarchical Generative Models | cs.LG | We present an architecture which lets us train deep, directed generative
models with many layers of latent variables. We include deterministic paths
between all latent variables and the generated output, and provide a richer set
of connections between computations for inference and generation, which enables
more effect... | computer science |
32,421 | Bayesian Optimization for Machine Learning : A Practical Guidebook | cs.LG | The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniqu... | computer science |
32,422 | Models, networks and algorithmic complexity | cs.LG | I aim to show that models, classification or generating functions,
invariances and datasets are algorithmically equivalent concepts once properly
defined, and provide some concrete examples of them. I then show that a) neural
networks (NNs) of different kinds can be seen to implement models, b) that
perturbations of in... | computer science |
32,423 | A new recurrent neural network based predictive model for Faecal
Calprotectin analysis: A retrospective study | cs.LG | Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation,
termed Inflammatory Bowel Disease (IBD), but not for cancer. In this
retrospective study of 804 patients, an enhanced benchmark predictive model for
analyzing FC is developed, based on a novel state-of-the-art Echo State Network
(ESN), an advan... | computer science |
32,424 | Quantization and Training of Low Bit-Width Convolutional Neural Networks
for Object Detection | cs.LG | We present LBW-Net, an efficient optimization based method for quantization
and training of the low bit-width convolutional neural networks (CNNs).
Specifically, we quantize the weights to zero or powers of two by minimizing
the Euclidean distance between full-precision weights and quantized weights
during backpropagat... | computer science |
32,425 | Supervised Learning for Optimal Power Flow as a Real-Time Proxy | cs.LG | In this work we design and compare different supervised learning algorithms
to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The
motivation for quick calculation of OPF cost outcomes stems from the growing
need of algorithmic-based long-term and medium-term planning methodologies in
power networks... | computer science |
32,426 | Temporal Feature Selection on Networked Time Series | cs.LG | This paper formulates the problem of learning discriminative features
(\textit{i.e.,} segments) from networked time series data considering the
linked information among time series. For example, social network users are
considered to be social sensors that continuously generate social signals
(tweets) represented as a ... | computer science |
32,427 | Robust Classification of Graph-Based Data | cs.LG | A graph-based classification method is proposed both for semi-supervised
learning in the case of Euclidean data and for classification in the case of
graph data. Our manifold learning technique is based on a convex optimization
problem involving a convex regularization term and a concave loss function with
a trade-off ... | computer science |
32,428 | Collaborative Filtering with User-Item Co-Autoregressive Models | cs.LG | Deep neural networks have shown promise in collaborative filtering (CF).
However, existing neural approaches are either user-based or item-based, which
cannot leverage all the underlying information explicitly. We propose CF-UIcA,
a neural co-autoregressive model for CF tasks, which exploits the structural
correlation ... | computer science |
32,429 | Loss is its own Reward: Self-Supervision for Reinforcement Learning | cs.LG | Reinforcement learning optimizes policies for expected cumulative reward.
Need the supervision be so narrow? Reward is delayed and sparse for many tasks,
making it a difficult and impoverished signal for end-to-end optimization. To
augment reward, we consider a range of self-supervised tasks that incorporate
states, ac... | computer science |
32,430 | A note on the function approximation error bound for risk-sensitive
reinforcement learning | cs.LG | In this paper we obtain several error bounds on function approximation for
the policy evaluation algorithm proposed by Basu et al. when the aim is to find
the risk-sensitive cost represented using exponential utility. We also give
examples where all our bounds achieve the "actual error" whereas the earlier
bound given ... | computer science |
32,431 | A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray
Classification | cs.LG | Gene expression data is widely used in disease analysis and cancer diagnosis.
However, since gene expression data could contain thousands of genes
simultaneously, successful microarray classification is rather difficult.
Feature selection is an important pre-treatment for any classification process.
Selecting a useful ... | computer science |
32,432 | Automatic composition and optimisation of multicomponent predictive
systems | cs.LG | Composition and parametrisation of multicomponent predictive systems (MCPSs)
consisting of chains of data transformation steps is a challenging task. This
paper is concerned with theoretical considerations and extensive experimental
analysis for automating the task of building such predictive systems. In the
theoretica... | computer science |
32,433 | Deep Learning and Hierarchal Generative Models | cs.LG | We propose a new prism for studying deep learning motivated by connections
between deep learning and evolution. We hypothesize that deep learning is
efficient in learning data from "generative hierarchal models". Our main
contributions are:
1. We introduce of a sequence of increasingly complex hierarchical generative... | computer science |
32,434 | Modeling documents with Generative Adversarial Networks | cs.LG | This paper describes a method for using Generative Adversarial Networks to
learn distributed representations of natural language documents. We propose a
model that is based on the recently proposed Energy-Based GAN, but instead uses
a Denoising Autoencoder as the discriminator network. Document representations
are extr... | computer science |
32,435 | Linear Learning with Sparse Data | cs.LG | Linear predictors are especially useful when the data is high-dimensional and
sparse. One of the standard techniques used to train a linear predictor is the
Averaged Stochastic Gradient Descent (ASGD) algorithm. We present an efficient
implementation of ASGD that avoids dense vector operations. We also describe a
trans... | computer science |
32,436 | Automatic Discoveries of Physical and Semantic Concepts via Association
Priors of Neuron Groups | cs.LG | The recent successful deep neural networks are largely trained in a
supervised manner. It {\it associates} complex patterns of input samples with
neurons in the last layer, which form representations of {\it concepts}. In
spite of their successes, the properties of complex patterns associated a
learned concept remain e... | computer science |
32,437 | Linking the Neural Machine Translation and the Prediction of Organic
Chemistry Reactions | cs.LG | Finding the main product of a chemical reaction is one of the important
problems of organic chemistry. This paper describes a method of applying a
neural machine translation model to the prediction of organic chemical
reactions. In order to translate 'reactants and reagents' to 'products', a
gated recurrent unit based ... | computer science |
32,438 | Parametric Learning and Monte Carlo Optimization | cs.LG | This paper uncovers and explores the close relationship between Monte Carlo
Optimization of a parametrized integral (MCO), Parametric machine-Learning
(PL), and `blackbox' or `oracle'-based optimization (BO). We make four
contributions. First, we prove that MCO is mathematically identical to a broad
class of PL problem... | computer science |
32,439 | Supervised Feature Selection via Dependence Estimation | cs.LG | We introduce a framework for filtering features that employs the
Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence
between the features and the labels. The key idea is that good features should
maximise such dependence. Feature selection for various supervised learning
problems (including classif... | computer science |
32,440 | Clustering with Transitive Distance and K-Means Duality | cs.LG | Recent spectral clustering methods are a propular and powerful technique for
data clustering. These methods need to solve the eigenproblem whose
computational complexity is $O(n^3)$, where $n$ is the number of data samples.
In this paper, a non-eigenproblem based clustering method is proposed to deal
with the clusterin... | computer science |
32,441 | Covariance and PCA for Categorical Variables | cs.LG | Covariances from categorical variables are defined using a regular simplex
expression for categories. The method follows the variance definition by Gini,
and it gives the covariance as a solution of simultaneous equations. The
calculated results give reasonable values for test data. A method of principal
component anal... | computer science |
32,442 | Asymptotic Analysis of Generative Semi-Supervised Learning | cs.LG | Semisupervised learning has emerged as a popular framework for improving
modeling accuracy while controlling labeling cost. Based on an extension of
stochastic composite likelihood we quantify the asymptotic accuracy of
generative semi-supervised learning. In doing so, we complement
distribution-free analysis by provid... | computer science |
32,443 | Unsupervised Supervised Learning II: Training Margin Based Classifiers
without Labels | cs.LG | Many popular linear classifiers, such as logistic regression, boosting, or
SVM, are trained by optimizing a margin-based risk function. Traditionally,
these risk functions are computed based on a labeled dataset. We develop a
novel technique for estimating such risks using only unlabeled data and the
marginal label dis... | computer science |
32,444 | Model Selection with the Loss Rank Principle | cs.LG | A key issue in statistics and machine learning is to automatically select the
"right" model complexity, e.g., the number of neighbors to be averaged over in
k nearest neighbor (kNN) regression or the polynomial degree in regression with
polynomials. We suggest a novel principle - the Loss Rank Principle (LoRP) -
for mo... | computer science |
32,445 | Statistical and Computational Tradeoffs in Stochastic Composite
Likelihood | cs.LG | Maximum likelihood estimators are often of limited practical use due to the
intensive computation they require. We propose a family of alternative
estimators that maximize a stochastic variation of the composite likelihood
function. Each of the estimators resolve the computation-accuracy tradeoff
differently, and taken... | computer science |
32,446 | Exponential Family Hybrid Semi-Supervised Learning | cs.LG | We present an approach to semi-supervised learning based on an exponential
family characterization. Our approach generalizes previous work on coupled
priors for hybrid generative/discriminative models. Our model is more flexible
and natural than previous approaches. Experimental results on several data sets
show that o... | computer science |
32,447 | A New Clustering Approach based on Page's Path Similarity for Navigation
Patterns Mining | cs.LG | In recent years, predicting the user's next request in web navigation has
received much attention. An information source to be used for dealing with such
problem is the left information by the previous web users stored at the web
access log on the web servers. Purposed systems for this problem work based on
this idea t... | computer science |
32,448 | Hierarchical Web Page Classification Based on a Topic Model and
Neighboring Pages Integration | cs.LG | Most Web page classification models typically apply the bag of words (BOW)
model to represent the feature space. The original BOW representation, however,
is unable to recognize semantic relationships between terms. One possible
solution is to apply the topic model approach based on the Latent Dirichlet
Allocation algo... | computer science |
32,449 | Supermartingales in Prediction with Expert Advice | cs.LG | We apply the method of defensive forecasting, based on the use of
game-theoretic supermartingales, to prediction with expert advice. In the
traditional setting of a countable number of experts and a finite number of
outcomes, the Defensive Forecasting Algorithm is very close to the well-known
Aggregating Algorithm. Not... | computer science |
32,450 | The Latent Bernoulli-Gauss Model for Data Analysis | cs.LG | We present a new latent-variable model employing a Gaussian mixture
integrated with a feature selection procedure (the Bernoulli part of the model)
which together form a "Latent Bernoulli-Gauss" distribution. The model is
applied to MAP estimation, clustering, feature selection and collaborative
filtering and fares fav... | computer science |
32,451 | Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de
signaux | cs.LG | We address in this paper the problem of multi-channel signal sequence
labeling. In particular, we consider the problem where the signals are
contaminated by noise or may present some dephasing with respect to their
labels. For that, we propose to jointly learn a SVM sample classifier with a
temporal filtering of the ch... | computer science |
32,452 | A note on sample complexity of learning binary output neural networks
under fixed input distributions | cs.LG | We show that the learning sample complexity of a sigmoidal neural network
constructed by Sontag (1992) required to achieve a given misclassification
error under a fixed purely atomic distribution can grow arbitrarily fast: for
any prescribed rate of growth there is an input distribution having this rate
as the sample c... | computer science |
32,453 | Reinforcement Learning via AIXI Approximation | cs.LG | This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. This approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the d... | computer science |
32,454 | Adapting to the Shifting Intent of Search Queries | cs.LG | Search engines today present results that are often oblivious to abrupt
shifts in intent. For example, the query `independence day' usually refers to a
US holiday, but the intent of this query abruptly changed during the release of
a major film by that name. While no studies exactly quantify the magnitude of
intent-shi... | computer science |
32,455 | Comparison of Support Vector Machine and Back Propagation Neural Network
in Evaluating the Enterprise Financial Distress | cs.LG | Recently, applying the novel data mining techniques for evaluating enterprise
financial distress has received much research alternation. Support Vector
Machine (SVM) and back propagation neural (BPN) network has been applied
successfully in many areas with excellent generalization results, such as rule
extraction, clas... | computer science |
32,456 | Close Clustering Based Automated Color Image Annotation | cs.LG | Most image-search approaches today are based on the text based tags
associated with the images which are mostly human generated and are subject to
various kinds of errors. The results of a query to the image database thus can
often be misleading and may not satisfy the requirements of the user. In this
work we propose ... | computer science |
32,457 | Bounded Coordinate-Descent for Biological Sequence Classification in
High Dimensional Predictor Space | cs.LG | We present a framework for discriminative sequence classification where the
learner works directly in the high dimensional predictor space of all
subsequences in the training set. This is possible by employing a new
coordinate-descent algorithm coupled with bounding the magnitude of the
gradient for selecting discrimin... | computer science |
32,458 | Semi-Supervised Kernel PCA | cs.LG | We present three generalisations of Kernel Principal Components Analysis
(KPCA) which incorporate knowledge of the class labels of a subset of the data
points. The first, MV-KPCA, penalises within class variances similar to Fisher
discriminant analysis. The second, LSKPCA is a hybrid of least squares
regression and ker... | computer science |
32,459 | Online Learning in Case of Unbounded Losses Using the Follow Perturbed
Leader Algorithm | cs.LG | In this paper the sequential prediction problem with expert advice is
considered for the case where losses of experts suffered at each step cannot be
bounded in advance. We present some modification of Kalai and Vempala algorithm
of following the perturbed leader where weights depend on past losses of the
experts. New ... | computer science |
32,460 | Switching between Hidden Markov Models using Fixed Share | cs.LG | In prediction with expert advice the goal is to design online prediction
algorithms that achieve small regret (additional loss on the whole data)
compared to a reference scheme. In the simplest such scheme one compares to the
loss of the best expert in hindsight. A more ambitious goal is to split the
data into segments... | computer science |
32,461 | Freezing and Sleeping: Tracking Experts that Learn by Evolving Past
Posteriors | cs.LG | A problem posed by Freund is how to efficiently track a small pool of experts
out of a much larger set. This problem was solved when Bousquet and Warmuth
introduced their mixing past posteriors (MPP) algorithm in 2001.
In Freund's problem the experts would normally be considered black boxes.
However, in this paper we... | computer science |
32,462 | Learning in embodied action-perception loops through exploration | cs.LG | Although exploratory behaviors are ubiquitous in the animal kingdom, their
computational underpinnings are still largely unknown. Behavioral Psychology
has identified learning as a primary drive underlying many exploratory
behaviors. Exploration is seen as a means for an animal to gather sensory data
useful for reducin... | computer science |
32,463 | An Identity for Kernel Ridge Regression | cs.LG | This paper derives an identity connecting the square loss of ridge regression
in on-line mode with the loss of the retrospectively best regressor. Some
corollaries about the properties of the cumulative loss of on-line ridge
regression are also obtained. | computer science |
32,464 | Bipartite ranking algorithm for classification and survival analysis | cs.LG | Unsupervised aggregation of independently built univariate predictors is
explored as an alternative regularization approach for noisy, sparse datasets.
Bipartite ranking algorithm Smooth Rank implementing this approach is
introduced. The advantages of this algorithm are demonstrated on two types of
problems. First, Smo... | computer science |
32,465 | Analysis and Extension of Arc-Cosine Kernels for Large Margin
Classification | cs.LG | We investigate a recently proposed family of positive-definite kernels that
mimic the computation in large neural networks. We examine the properties of
these kernels using tools from differential geometry; specifically, we analyze
the geometry of surfaces in Hilbert space that are induced by these kernels.
When this g... | computer science |
32,466 | Nonnegative Matrix Factorization for Semi-supervised Dimensionality
Reduction | cs.LG | We show how to incorporate information from labeled examples into nonnegative
matrix factorization (NMF), a popular unsupervised learning algorithm for
dimensionality reduction. In addition to mapping the data into a space of lower
dimensionality, our approach aims to preserve the nonnegative components of the
data tha... | computer science |
32,467 | Clustering and Latent Semantic Indexing Aspects of the Nonnegative
Matrix Factorization | cs.LG | This paper provides a theoretical support for clustering aspect of the
nonnegative matrix factorization (NMF). By utilizing the Karush-Kuhn-Tucker
optimality conditions, we show that NMF objective is equivalent to graph
clustering objective, so clustering aspect of the NMF has a solid
justification. Different from prev... | computer science |
32,468 | Evaluation of Performance Measures for Classifiers Comparison | cs.LG | The selection of the best classification algorithm for a given dataset is a
very widespread problem, occuring each time one has to choose a classifier to
solve a real-world problem. It is also a complex task with many important
methodological decisions to make. Among those, one of the most crucial is the
choice of an a... | computer science |
32,469 | Modeling transition dynamics in MDPs with RKHS embeddings of conditional
distributions | cs.LG | We propose a new, nonparametric approach to estimating the value function in
reinforcement learning. This approach makes use of a recently developed
representation of conditional distributions as functions in a reproducing
kernel Hilbert space. Such representations bypass the need for estimating
transition probabilitie... | computer science |
32,470 | Combining One-Class Classifiers via Meta-Learning | cs.LG | Selecting the best classifier among the available ones is a difficult task,
especially when only instances of one class exist. In this work we examine the
notion of combining one-class classifiers as an alternative for selecting the
best classifier. In particular, we propose two new one-class classification
performance... | computer science |
32,471 | Building high-level features using large scale unsupervised learning | cs.LG | We consider the problem of building high-level, class-specific feature
detectors from only unlabeled data. For example, is it possible to learn a face
detector using only unlabeled images? To answer this, we train a 9-layered
locally connected sparse autoencoder with pooling and local contrast
normalization on a large ... | computer science |
32,472 | Two-Manifold Problems | cs.LG | Recently, there has been much interest in spectral approaches to learning
manifolds---so-called kernel eigenmap methods. These methods have had some
successes, but their applicability is limited because they are not robust to
noise. To address this limitation, we look at two-manifold problems, in which
we simultaneousl... | computer science |
32,473 | Extension of TSVM to Multi-Class and Hierarchical Text Classification
Problems With General Losses | cs.LG | Transductive SVM (TSVM) is a well known semi-supervised large margin learning
method for binary text classification. In this paper we extend this method to
multi-class and hierarchical classification problems. We point out that the
determination of labels of unlabeled examples with fixed classifier weights is
a linear ... | computer science |
32,474 | K-Plane Regression | cs.LG | In this paper, we present a novel algorithm for piecewise linear regression
which can learn continuous as well as discontinuous piecewise linear functions.
The main idea is to repeatedly partition the data and learn a liner model in in
each partition. While a simple algorithm incorporating this idea does not work
well,... | computer science |
32,475 | Algorithm for Missing Values Imputation in Categorical Data with Use of
Association Rules | cs.LG | This paper presents algorithm for missing values imputation in categorical
data. The algorithm is based on using association rules and is presented in
three variants. Experimental shows better accuracy of missing values imputation
using the algorithm then using most common attribute value. | computer science |
32,476 | No-Regret Algorithms for Unconstrained Online Convex Optimization | cs.LG | Some of the most compelling applications of online convex optimization,
including online prediction and classification, are unconstrained: the natural
feasible set is R^n. Existing algorithms fail to achieve sub-linear regret in
this setting unless constraints on the comparator point x^* are known in
advance. We presen... | computer science |
32,477 | Recovering the Optimal Solution by Dual Random Projection | cs.LG | Random projection has been widely used in data classification. It maps
high-dimensional data into a low-dimensional subspace in order to reduce the
computational cost in solving the related optimization problem. While previous
studies are focused on analyzing the classification performance of using random
projection, i... | computer science |
32,478 | On the difficulty of training Recurrent Neural Networks | cs.LG | There are two widely known issues with properly training Recurrent Neural
Networks, the vanishing and the exploding gradient problems detailed in Bengio
et al. (1994). In this paper we attempt to improve the understanding of the
underlying issues by exploring these problems from an analytical, a geometric
and a dynamic... | computer science |
32,479 | An Approach of Improving Students Academic Performance by using k means
clustering algorithm and Decision tree | cs.LG | Improving students academic performance is not an easy task for the academic
community of higher learning. The academic performance of engineering and
science students during their first year at university is a turning point in
their educational path and usually encroaches on their General Point
Average,GPA in a decisi... | computer science |
32,480 | Multi-Target Regression via Input Space Expansion: Treating Targets as
Inputs | cs.LG | In many practical applications of supervised learning the task involves the
prediction of multiple target variables from a common set of input variables.
When the prediction targets are binary the task is called multi-label
classification, while when the targets are continuous the task is called
multi-target regression... | computer science |
32,481 | Exploratory Learning | cs.LG | In multiclass semi-supervised learning (SSL), it is sometimes the case that
the number of classes present in the data is not known, and hence no labeled
examples are provided for some classes. In this paper we present variants of
well-known semi-supervised multiclass learning methods that are robust when the
data conta... | computer science |
32,482 | A PAC-Bayesian Tutorial with A Dropout Bound | cs.LG | This tutorial gives a concise overview of existing PAC-Bayesian theory
focusing on three generalization bounds. The first is an Occam bound which
handles rules with finite precision parameters and which states that
generalization loss is near training loss when the number of bits needed to
write the rule is small compa... | computer science |
32,483 | Minimum Error Rate Training and the Convex Hull Semiring | cs.LG | We describe the line search used in the minimum error rate training algorithm
MERT as the "inside score" of a weighted proof forest under a semiring defined
in terms of well-understood operations from computational geometry. This
conception leads to a straightforward complexity analysis of the dynamic
programming MERT ... | computer science |
32,484 | Large-scale Multi-label Learning with Missing Labels | cs.LG | The multi-label classification problem has generated significant interest in
recent years. However, existing approaches do not adequately address two key
challenges: (a) the ability to tackle problems with a large number (say
millions) of labels, and (b) the ability to handle data with missing labels. In
this paper, we... | computer science |
32,485 | Towards Distribution-Free Multi-Armed Bandits with Combinatorial
Strategies | cs.LG | In this paper we study a generalized version of classical multi-armed bandits
(MABs) problem by allowing for arbitrary constraints on constituent bandits at
each decision point. The motivation of this study comes from many situations
that involve repeatedly making choices subject to arbitrary constraints in an
uncertai... | computer science |
32,486 | A scalable stage-wise approach to large-margin multi-class loss based
boosting | cs.LG | We present a scalable and effective classification model to train multi-class
boosting for multi-class classification problems. Shen and Hao introduced a
direct formulation of multi- class boosting in the sense that it directly
maximizes the multi- class margin [C. Shen and Z. Hao, "A direct formulation
for totally-cor... | computer science |
32,487 | A New Strategy of Cost-Free Learning in the Class Imbalance Problem | cs.LG | In this work, we define cost-free learning (CFL) formally in comparison with
cost-sensitive learning (CSL). The main difference between them is that a CFL
approach seeks optimal classification results without requiring any cost
information, even in the class imbalance problem. In fact, several CFL
approaches exist in t... | computer science |
32,488 | A Propound Method for the Improvement of Cluster Quality | cs.LG | In this paper Knockout Refinement Algorithm (KRA) is proposed to refine
original clusters obtained by applying SOM and K-Means clustering algorithms.
KRA Algorithm is based on Contingency Table concepts. Metrics are computed for
the Original and Refined Clusters. Quality of Original and Refined Clusters are
compared in... | computer science |
32,489 | Towards Minimax Online Learning with Unknown Time Horizon | cs.LG | We consider online learning when the time horizon is unknown. We apply a
minimax analysis, beginning with the fixed horizon case, and then moving on to
two unknown-horizon settings, one that assumes the horizon is chosen randomly
according to some known distribution, and the other which allows the adversary
full contro... | computer science |
32,490 | The Planning-ahead SMO Algorithm | cs.LG | The sequential minimal optimization (SMO) algorithm and variants thereof are
the de facto standard method for solving large quadratic programs for support
vector machine (SVM) training. In this paper we propose a simple yet powerful
modification. The main emphasis is on an algorithm improving the SMO step size
by plann... | computer science |
32,491 | Clustering on Multiple Incomplete Datasets via Collective Kernel
Learning | cs.LG | Multiple datasets containing different types of features may be available for
a given task. For instance, users' profiles can be used to group users for
recommendation systems. In addition, a model can also use users' historical
behaviors and credit history to group users. Each dataset contains different
information an... | computer science |
32,492 | Fast Multi-Instance Multi-Label Learning | cs.LG | In many real-world tasks, particularly those involving data objects with
complicated semantics such as images and texts, one object can be represented
by multiple instances and simultaneously be associated with multiple labels.
Such tasks can be formulated as multi-instance multi-label learning (MIML)
problems, and hav... | computer science |
32,493 | Localized Iterative Methods for Interpolation in Graph Structured Data | cs.LG | In this paper, we present two localized graph filtering based methods for
interpolating graph signals defined on the vertices of arbitrary graphs from
only a partial set of samples. The first method is an extension of previous
work on reconstructing bandlimited graph signals from partially observed
samples. The iterati... | computer science |
32,494 | Scaling Graph-based Semi Supervised Learning to Large Number of Labels
Using Count-Min Sketch | cs.LG | Graph-based Semi-supervised learning (SSL) algorithms have been successfully
used in a large number of applications. These methods classify initially
unlabeled nodes by propagating label information over the structure of graph
starting from seed nodes. Graph-based SSL algorithms usually scale linearly
with the number o... | computer science |
32,495 | Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear
Spectral Penalties | cs.LG | We present a general framework to learn functions in tensor product
reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a
novel representer theorem suitable for existing as well as new spectral
penalties for tensors. When the functions in the TP-RKHS are defined on the
Cartesian product of finite ... | computer science |
32,496 | Thompson Sampling in Dynamic Systems for Contextual Bandit Problems | cs.LG | We consider the multiarm bandit problems in the timevarying dynamic system
for rich structural features. For the nonlinear dynamic model, we propose the
approximate inference for the posterior distributions based on Laplace
Approximation. For the context bandit problems, Thompson Sampling is adopted
based on the underl... | computer science |
32,497 | Graph-Based Approaches to Clustering Network-Constrained Trajectory Data | cs.LG | Clustering trajectory data attracted considerable attention in the last few
years. Most of prior work assumed that moving objects can move freely in an
euclidean space and did not consider the eventual presence of an underlying
road network and its influence on evaluating the similarity between
trajectories. In this pa... | computer science |
32,498 | Multi-Task Regularization with Covariance Dictionary for Linear
Classifiers | cs.LG | In this paper we propose a multi-task linear classifier learning problem
called D-SVM (Dictionary SVM). D-SVM uses a dictionary of parameter covariance
shared by all tasks to do multi-task knowledge transfer among different tasks.
We formally define the learning problem of D-SVM and show two interpretations
of this pro... | computer science |
32,499 | Learning Theory and Algorithms for Revenue Optimization in Second-Price
Auctions with Reserve | cs.LG | Second-price auctions with reserve play a critical role for modern search
engine and popular online sites since the revenue of these companies often
directly de- pends on the outcome of such auctions. The choice of the reserve
price is the main mechanism through which the auction revenue can be influenced
in these elec... | computer science |
32,500 | Relative Deviation Learning Bounds and Generalization with Unbounded
Loss Functions | cs.LG | We present an extensive analysis of relative deviation bounds, including
detailed proofs of two-sided inequalities and their implications. We also give
detailed proofs of two-sided generalization bounds that hold in the general
case of unbounded loss functions, under the assumption that a moment of the
loss is bounded.... | computer science |
32,501 | Efficient Optimization for Sparse Gaussian Process Regression | cs.LG | We propose an efficient optimization algorithm for selecting a subset of
training data to induce sparsity for Gaussian process regression. The algorithm
estimates an inducing set and the hyperparameters using a single objective,
either the marginal likelihood or a variational free energy. The space and time
complexity ... | computer science |
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